Hello community,
here is the log from the commit of package ghc-hstatistics for openSUSE:Factory
checked in at 2017-03-29 13:24:58
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/ghc-hstatistics (Old)
and /work/SRC/openSUSE:Factory/.ghc-hstatistics.new (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "ghc-hstatistics"
Wed Mar 29 13:24:58 2017 rev:2 rq:480943 version:0.3
Changes:
--------
--- /work/SRC/openSUSE:Factory/ghc-hstatistics/ghc-hstatistics.changes
2017-03-10 21:38:29.520927118 +0100
+++ /work/SRC/openSUSE:Factory/.ghc-hstatistics.new/ghc-hstatistics.changes
2017-03-29 13:24:58.944965138 +0200
@@ -1,0 +2,10 @@
+Sun Feb 12 14:15:04 UTC 2017 - psim...@suse.com
+
+- Update to version 0.3 with cabal2obs.
+
+-------------------------------------------------------------------
+Mon Nov 14 09:29:49 UTC 2016 - psim...@suse.com
+
+- Update to version 0.2.5.4 with cabal2obs.
+
+-------------------------------------------------------------------
Old:
----
hstatistics-0.2.5.3.tar.gz
New:
----
hstatistics-0.3.tar.gz
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ ghc-hstatistics.spec ++++++
--- /var/tmp/diff_new_pack.g1sA4A/_old 2017-03-29 13:24:59.632867850 +0200
+++ /var/tmp/diff_new_pack.g1sA4A/_new 2017-03-29 13:24:59.636867283 +0200
@@ -1,7 +1,7 @@
#
# spec file for package ghc-hstatistics
#
-# Copyright (c) 2016 SUSE LINUX GmbH, Nuernberg, Germany.
+# Copyright (c) 2017 SUSE LINUX GmbH, Nuernberg, Germany.
#
# All modifications and additions to the file contributed by third parties
# remain the property of their copyright owners, unless otherwise agreed
@@ -18,15 +18,14 @@
%global pkg_name hstatistics
Name: ghc-%{pkg_name}
-Version: 0.2.5.3
+Version: 0.3
Release: 0
Summary: Statistics
License: BSD-3-Clause
-Group: System/Libraries
+Group: Development/Languages/Other
Url: https://hackage.haskell.org/package/%{pkg_name}
Source0:
https://hackage.haskell.org/package/%{pkg_name}-%{version}/%{pkg_name}-%{version}.tar.gz
BuildRequires: ghc-Cabal-devel
-# Begin cabal-rpm deps:
BuildRequires: ghc-array-devel
BuildRequires: ghc-hmatrix-devel
BuildRequires: ghc-hmatrix-gsl-stats-devel
@@ -34,7 +33,6 @@
BuildRequires: ghc-rpm-macros
BuildRequires: ghc-vector-devel
BuildRoot: %{_tmppath}/%{name}-%{version}-build
-# End cabal-rpm deps
%description
Purely functional interface for statistics based on hmatrix and
@@ -60,15 +58,12 @@
%prep
%setup -q -n %{pkg_name}-%{version}
-
%build
%ghc_lib_build
-
%install
%ghc_lib_install
-
%post devel
%ghc_pkg_recache
@@ -81,6 +76,6 @@
%files devel -f %{name}-devel.files
%defattr(-,root,root,-)
-%doc README
+%doc CHANGES README
%changelog
++++++ hstatistics-0.2.5.3.tar.gz -> hstatistics-0.3.tar.gz ++++++
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/hstatistics-0.2.5.3/CHANGES
new/hstatistics-0.3/CHANGES
--- old/hstatistics-0.2.5.3/CHANGES 2015-10-10 07:49:48.000000000 +0200
+++ new/hstatistics-0.3/CHANGES 2017-02-06 02:48:46.000000000 +0100
@@ -106,3 +106,10 @@
0.2.5.3:
updated for hmatrix 0.17
+
+0.2.5.4:
+ updated for hmatrix 0.18
+
+0.3:
+ changed PCA to use SVD as suggested by Pavol Klacansky
+ (issue #3)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/hstatistics-0.2.5.3/hstatistics.cabal
new/hstatistics-0.3/hstatistics.cabal
--- old/hstatistics-0.2.5.3/hstatistics.cabal 2015-10-10 07:49:48.000000000
+0200
+++ new/hstatistics-0.3/hstatistics.cabal 2017-02-06 02:48:46.000000000
+0100
@@ -1,8 +1,8 @@
Name: hstatistics
-Version: 0.2.5.3
+Version: 0.3
License: BSD3
License-file: LICENSE
-Copyright: (c) A.V.H. McPhail 2010, 2011, 2012, 2013, 2014
+Copyright: (c) A.V.H. McPhail 2010--2014, 2016, 2017
Author: Vivian McPhail
Maintainer: haskell.vivian.mcphail <at> gmail <dot> com
Stability: provisional
@@ -16,7 +16,7 @@
.
Feature requests, suggestions, and bug fixes welcome.
Category: Math, Statistics
-tested-with: GHC ==7.10.2
+tested-with: GHC ==8.0.1
cabal-version: >=1.8
@@ -30,8 +30,8 @@
Build-Depends: base >= 4 && < 5,
array, random,
vector,
- hmatrix >= 0.17,
- hmatrix-gsl-stats >= 0.4
+ hmatrix >= 0.18,
+ hmatrix-gsl-stats >= 0.4.1.6
Extensions:
@@ -46,12 +46,9 @@
other-modules:
C-sources:
- ghc-prof-options: -auto
-
ghc-options: -Wall -fno-warn-missing-signatures
-fno-warn-orphans
-fno-warn-unused-binds
- -O2
source-repository head
type: git
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/hstatistics-0.2.5.3/lib/Numeric/Statistics/PCA.hs
new/hstatistics-0.3/lib/Numeric/Statistics/PCA.hs
--- old/hstatistics-0.2.5.3/lib/Numeric/Statistics/PCA.hs 2015-10-10
07:49:48.000000000 +0200
+++ new/hstatistics-0.3/lib/Numeric/Statistics/PCA.hs 2017-02-06
02:48:46.000000000 +0100
@@ -1,7 +1,7 @@
-----------------------------------------------------------------------------
-- |
-- Module : Numeric.Statistics.PCA
--- Copyright : (c) A. V. H. McPhail 2010, 2014
+-- Copyright : (c) A. V. H. McPhail 2010, 2014, 2017
-- License : BSD3
--
-- Maintainer : haskell.vivian.mcphail <at> gmail <dot> com
@@ -27,38 +27,47 @@
import Numeric.GSL.Statistics
-import Numeric.Statistics
+--import Numeric.Statistics
-----------------------------------------------------------------------------
-- | find the principal components of multidimensional data greater than
-- the threshhold
-pca :: I.Array Int (Vector Double) -- the data
+pca :: I.Array Int (Vector Double) -- the data
-> Double -- eigenvalue threshold
- -> Matrix Double
+ -> (Vector Double, Matrix Double) -- Eignevalues, Principal components
pca d q = let d' = fmap (\x -> x - (scalar $ mean x)) d -- remove the mean
from each dimension
- cv = covarianceMatrix d'
- (val',vec') = eigSH $ trustSym cv -- the covariance matrix is
real symmetric
- val = toList val'
- vec = toColumns vec'
- v' = zip val vec
+ d'' = fromColumns $ I.elems d'
+ (_,vec',uni') = svd d''
+ vec = toList vec'
+ uni = toColumns uni'
+ v' = zip vec uni
v = filter (\(x,_) -> x > q) v' -- keep only eigens > than
parameter
- in fromColumns $ snd $ unzip v
+ (eigs,vs) = unzip v
+ in (fromList eigs,fromColumns vs)
-- | find N greatest principal components of multidimensional data
-- according to size of the eigenvalue
pcaN :: I.Array Int (Vector Double) -- the data
-> Int -- number of components to return
- -> Matrix Double
+ -> (Vector Double, Matrix Double) -- Eignevalues, Principal components
pcaN d n = let d' = fmap (\x -> x - (scalar $ mean x)) d -- remove the mean
from each dimension
- cv = covarianceMatrix d'
- (val',vec') = eigSH $ trustSym cv -- the covariance matrix is
real symmetric
- val = toList val'
- vec = toColumns vec'
- v' = zip val vec
+ d'' = fromColumns $ I.elems d'
+ (_,vec',uni') = svd d''
+ vec = toList vec'
+ uni = toColumns uni'
+ v' = zip vec uni
v = take n $ reverse $ sortBy (comparing fst) v'
- in fromColumns $ snd $ unzip v
+ (eigs,vs) = unzip v
+ in (fromList eigs,fromColumns vs)
+v1 = fromList [1,2,3,4,5,6::Double]
+v2 = fromList [2,3,4,5,6,7::Double]
+v3 = fromList [3,4,5,6,7,8::Double]
+
+a = fromColumns [v1,v2,v3]
+b = I.listArray (1,3::Int) [v1,v2,v3] :: I.Array Int (Vector Double)
+
-- | perform a PCA transform of the original data (remove mean)
-- | Final = M^T Data^T
pcaTransform :: I.Array Int (Vector Double) -- ^ the data
@@ -74,11 +83,11 @@
-> I.Array Int (Vector Double) -- ^ the reduced data
pcaReduce d q = let u = fmap (scalar . mean) d
d' = zipWith (-) (I.elems d) (I.elems u)
- cv = covarianceMatrix $ I.listArray (I.bounds d) d'
- (val',vec') = eigSH $ trustSym cv -- the covariance matrix
is real symmetric
- val = toList val'
- vec = toColumns vec'
- v' = zip val vec
+ d'' = fromColumns d'
+ (_,vec',uni') = svd d''
+ vec = toList vec'
+ uni = toColumns uni'
+ v' = zip vec uni
v = filter (\(x,_) -> x > q) v' -- keep only eigens >
than parameter
m = fromColumns $ snd $ unzip v
in I.listArray (I.bounds d) $ zipWith (+) (toRows $ m <> (tr'
m) <> fromRows d') (I.elems u)
@@ -89,11 +98,11 @@
-> I.Array Int (Vector Double) -- ^ the reduced data, with n
principal components
pcaReduceN d n = let u = fmap (scalar . mean) d
d' = zipWith (-) (I.elems d) (I.elems u)
- cv = covarianceMatrix $ I.listArray (I.bounds d) d'
- (val',vec') = eigSH $ trustSym cv -- the covariance
matrix is real symmetric
- val = toList val'
- vec = toColumns vec'
- v' = zip val vec
+ d'' = fromColumns d'
+ (_,vec',uni') = svd d''
+ vec = toList vec'
+ uni = toColumns uni'
+ v' = zip vec uni
v = take n $ reverse $ sortBy (comparing fst) v'
m = fromColumns $ snd $ unzip v
in I.listArray (I.bounds d) $ zipWith (+) (toRows $ m <>
(tr' m) <> fromRows d') (I.elems u)
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/hstatistics-0.2.5.3/lib/Numeric/Statistics.hs
new/hstatistics-0.3/lib/Numeric/Statistics.hs
--- old/hstatistics-0.2.5.3/lib/Numeric/Statistics.hs 2015-10-10
07:49:48.000000000 +0200
+++ new/hstatistics-0.3/lib/Numeric/Statistics.hs 2017-02-06
02:48:46.000000000 +0100
@@ -79,7 +79,7 @@
meanArray a = meanList $ I.elems a
-- | the mean of a matrix with data series in rows
-meanMatrix :: (Container Vector a, Num (Vector a), Element a, Fractional a) =>
Matrix a -> Sample a
+meanMatrix :: (Container Vector a, Num (Vector a), Fractional a) => Matrix a
-> Sample a
meanMatrix a = meanList $ toRows a
-----------------------------------------------------------------------------
@@ -93,11 +93,11 @@
in mxs - msx
-- | the variance of an array of vectors
-varianceArray :: (Container Vector a, Floating (Vector a), Num a, Fractional
a) => Samples a -> Sample a
+varianceArray :: (Container Vector a, Floating (Vector a), Fractional a) =>
Samples a -> Sample a
varianceArray a = varianceList $ I.elems a
-- | the variance of a matrix with data series in rows
-varianceMatrix :: (Container Vector a, Floating (Vector a), Element a, Num a,
Fractional a) => Matrix a -> Sample a
+varianceMatrix :: (Container Vector a, Floating (Vector a), Fractional a) =>
Matrix a -> Sample a
varianceMatrix a = varianceList $ toRows a
-----------------------------------------------------------------------------
@@ -253,7 +253,7 @@
-----------------------------------------------------------------------------
-- | count the number of runs greater than or equal to @n@ in the data
-run_count :: (Num a, Num t, Ord b, Ord a, Storable b, Container Vector b)
+run_count :: (Num a, Num t, Ord b, Ord a, Container Vector b)
=> a -- ^ longest run to count
-> Vector b -- ^ data
-> [(a, t)] -- ^ [(run length,count)]
